An Efficient Detour Computation Scheme for Electric Vehicles to Support Smart Cities’ Electrification

Abstract

Achieving carbon-neutral transportation is the ultimate goal of the ongoing joint efforts of governments, policy-makers, and the transportation research community. Electrification of smart cities is a very important step towards the above objective; therefore, accelerating the adoption and widening the use of Electric Vehicles (EVs) are required. However, to achieve the full potential of EVs, ground-breaking detour computation and charging station selection schemes are needed. To this end, this paper developed a new scheme that finds the most suitable detour/route for an EV whenever an unexpected event occurs on the road. This scheme is based on A* and uses an original, Simple-Additive-Weighting (SAW)-based, charging station selection method. The performance evaluation carried out using the open-source traffic simulation platform SUMO under a grid map, as well as a real road network map highlighted that our scheme ensured more than 99% of EVs will reach their destination within a reasonable time even if a battery recharge is needed. This is a significant improvement compared to the baseline scheme that uses the A* only.

Electrification of Smart Cities

“…In another work of this Special Issue, Mansfield et al [2] claimed that achieving carbonneutral transportation is the ultimate goal of the ongoing joint efforts of governments, policy makers, and the transportation research community. The electrification of smart cities is a very important step towards the above objective; therefore, accelerating the adoption and widening the use of electric vehicles are required.…”

“…However, to achieve the full potential of electric vehicles, ground-breaking detour computation and charging station selection schemes are needed. Hence, Mansfield et al [2] developed a new scheme that finds the most suitable detour/route for electric vehicles whenever an unexpected event occurs on the road. This scheme is based on A* and uses an original, Simple Additive Weighting (SAW)-based charging station selection method.…”

A Reliability-Based Network Equilibrium Model with Electric Vehicles and Gasoline Vehicles

“…To predict the traffic flow pattern of the transportation network with EVs, it is important to understand their path choice behaviour. EV travellers' path choice behaviour is often considered different from that of GV travellers due to range anxiety, en-route recharge requirements and insufficient charging infrastructure [17,18]. For example, EVs may opt for detours to pass by charging stations in order to avoid running out of charge.…”